This invention relates to a method and apparatus for classifying defect images obtained by imaging of contaminations or defects detected on semiconductor wafers and a semiconductor device manufacturing process based on the method and apparatus.
A semiconductor device is manufactured by making a wafer as a substrate go through several processing steps including exposure, development and etching. After a specific step among these processing steps is over, the locations and sizes of contaminations or defects on the wafer (hereinafter collectively referred to as “defects”) are checked by a particle inspection system or a defect inspection system. Enlarged images of all or some of the contaminations or defects detected by this inspection are produced using a magnifying imaging device such as an optical microscope or SEM (Scanning Electron Microscope) to obtain detailed information on their size, shape and texture (surface pattern). This information has been used in order to identify the step which is the particle or defect source.
In recent years, a magnifying imaging system which incorporates a function to automatically produce enlarged images of contaminations or defects based on inspection result data obtained from the particle or defect inspection system, or ADR (Auto Defect Review), has been developed, which has made it possible to collect a huge volume of image data in a shorter time. However, as an increasing number of images are obtained, operating personnel must consume a greater amount of time and energy to analyze the images. In order to improve the working efficiency in such image analyses, a function to automatically classify the obtained defect images into categories according to their features, ADC (Auto Defect Classification), has been developed. Automatic classification of defect images using this function has contributed to improvement in analysis working efficiency by allowing operating personnel to analyze only defect images of a category to be noted or make an analysis paying attention only to the number of defects in each category.
A method to realize ADC has been disclosed in Japanese Published Unexamined Patent Application No. Hei 8-21803. In this method, a standard image for each defect category, called “teacher image data,” is prepared and this data is used to execute a neural network learning session; then, for automatic classification, the data thus learned is used to decide to which category each input defect image should belong.
An example of a method to realize the function to automatically produce enlarged images of contaminations or defects based on inspection result data obtained from the particle or defect inspection system, or ADR, has been disclosed in Japanese Published Unexamined Patent Application No. Hei 9-139406 in which secondary electronic images from an electron microscope are used to observe contaminations or defects at optimum magnification ratios based on inspection result data obtained from the particle or defect inspection system.
However, in order to realize a system for auto defect classification as mentioned above, the user must teach the system the feature of each category by some means, whether a neural network as mentioned above is used or not, or whatever category judgment algorithm is used. In other words, it is necessary to teach the system the relationships between images and the categories they should belong to, for example, in the following ways: the user observes a defect image and decides to which category it should belong, or the user prepares data which describes the features of an image group which the user considers as belonging to a category. As the user handles more images to prepare higher quality teaching data, the user has to spend more time and energy in this work. Thus there is a need for an efficient way to provide the system with teaching data.
In addition, after teaching has occurred and automatic classification has been done, there still is the problem of effectively presenting the defect data to the user for analysis. Thus there is a need for an effective display of classified defect image data, so that the user may detect/evaluate the defects and, if needed, improve parts of the semiconductor device manufacturing process.